Simulation Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies

<p>This is a supplementary simulation dataset to reproduce Fig. 3C,D of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <p>The zip folder contains the following 3 files in h5 format:&nbsp;front attack (out_Npred1_pred_angle0.0.h5), side attack (<span>out_Npred1_pred_angle1.5707963267948966.h5), </span><span>back attack (out_Npred1_pred_angle3.141592653589793.h5).</span></p> <p>Each file has the following "keys":&nbsp;KeysViewHDF5 ['circ_seg', 'end', 'endD', 'end_PosVel', 'fount', 'part', 'partD', 'pavas', 'pred', 'predD', 'start', 'start_fountain', 'start_pred', 'swarm', 'swarm_pred0', 'swarm_predD'].</p> <p>The key necessary to reproduce Fig. 3C,D of the aforementioned paper is "fount" (&lt;HDF5 dataset "fount": shape (40, 1200, 100, 8), type "&lt;f8"&gt;).&nbsp;Namely, "fount" data array consists of 40 simulation runs, 1200 time points, 100 agents, and 8 metrics. The metric&nbsp;with index "0" depicts the value of the Euclidean distance from the agent <em>i</em> to the simulated predator. The metric&nbsp;with index "1" depicts the value of the position angle (theta 1 in rad) of the agent <em>i</em> relative to the simulated predator. The metric&nbsp;with index "2" depicts the value of the flee angle (theta 2 in rad) of the agent <em>i</em> relative to the simulated predator.</p> <p>To estimate the start and the end of the fountain evasion, one can use the following script in Python:</p> <p>import numpy as np</p> <p>m = h5py.File(filename, "r")<br><br>for key in m.keys():<br>&nbsp;&nbsp;&nbsp;print(key)</p> <p>fount_runs = m[key]["fount"]</p> <p>for j in range(40):<br>&nbsp;&nbsp;&nbsp;fnt_start[j]&nbsp; =&nbsp; &nbsp;np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][0]&nbsp;&nbsp;<br>&nbsp;&nbsp;&nbsp;fnt_end[j]&nbsp; &nbsp;= &nbsp;&nbsp;np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][-1]</p>